Synthetic data, or data that is artificially manufactured rather than generated by real-world events, is a promising technology for helping healthcare organizations to share knowledge while protecting individual privacy. Sharing data safely is one of the biggest challenges in the healthcare industry today. Source: Synthetic data can help power AI applications at a fraction of the time and cost Healthcare Synthetic data combines techniques from the movie and gaming industries (simulation, CGI) with generative deep neural networks (GANs, VAEs), allowing car manufacturers to engineer realistic datasets and simulated environments at scale without driving in the real world. While a handful of companies may be able to afford the process of producing and testing millions of vehicles in various geographical environments, most OEMs do not have sufficient resources or vehicles with the capability to provide such datasets. Automakers and autonomous vehicle (AV) manufacturers use real world data to train, test, and validate roadway driver safety monitoring systems. Self-driving autonomous technology can dramatically reduce collision rates resulting from distracted driving. Now, let's have a look at some of the most popular applications for synthetic data in computer vision. Four synthetic data applications in computer vision It is an open-source Python framework that allows you to create photo-realistic synthetic data. On, Google researchers Klaus Greff, Francois Belletti, and Lucas Beyer released their research paper on Kubric: A scalable dataset generator.
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